Joaquín González-Rodríguez

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A function-based approach to on-line signature verification is presented. The system uses a set of time sequences and Hidden Markov Models (HMMs). Development and evaluation experiments are reported on a subcorpus of the MCYT bimodal biometric database comprising more than 7,000 signatures from 145 subjects. The system is compared to other state-of-the-art(More)
Score normalization methods in biometric verification, which encompass the more traditional user-dependent decision thresholding techniques, are reviewed from a test hypotheses point of view. These are classified into test dependent and target dependent methods. The focus of the paper is on target dependent score normalization techniques, which are further(More)
The baseline copus of a new multimodal database, acquired in the framework of the FP6 EU BioSec Integrated Project, is presented. The corpus consist of fingerprint images acquired with three different sensors, frontal face images from a webcam, iris images from an iris sensor, and voice utterances acquired both with a close-talk headset and a distant webcam(More)
Securing the exchange of intellectual property and providing protection to multimedia contents in distribution systems have enabled the advent of digital rights management (DRM) systems. User authentication, a key component of any DRM system, ensures that only those with specific rights are able to access the digital information. It is here that biometrics(More)
A novel kernel-based fusion strategy is presented. It is based on SVM classifiers, trade-off coefficients introduced in the standard SVM training and testing procedures, and quality measures of the input biometric signals. Experimental results on a prototype application based on voice and fingerprint traits are reported. The benefits of using the two(More)
One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint(More)
Forensic DNA profiling is acknowledged as the model for a scientifically defensible approach in forensic identification science, as it meets the most stringent court admissibility requirements demanding transparency in scientific evaluation of evidence and testability of systems and protocols. In this paper, we propose a unified approach to forensic speaker(More)
This work studies the use of deep neural networks (DNNs) to address automatic language identification (LID). Motivated by their recent success in acoustic modelling, we adapt DNNs to the problem of identifying the language of a given spoken utterance from short-term acoustic features. The proposed approach is compared to state-of-the-art i-vector based(More)
In this contribution, the Bayesian framework for interpretation of evidence when applied to forensic speaker recognition is introduced. Different aspects of the use of voice as evidence in the court are addressed, as well as the use by the forensic expert of the likelihood ratio as the right way to express the strength of the evidence. Details on(More)
This work explores the use of Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) for automatic language identification (LID). The use of RNNs is motivated by their better ability in modeling sequences with respect to feed forward networks used in previous works. We show that LSTM RNNs can effectively exploit temporal dependencies in acoustic(More)